SPATIO-TEMPORAL CHANGE IN SNOW COVER AREA USING RS & GIS IN THE GORI GANGA WATERSHED,

Present research paper is an attempt to examine the dynamics of snow cover by using Normalized Difference Snow Index (NDSI) in Gori Ganga watershed, Kumaun Himalaya, Uttarakhand (India). For the study of snow cover of Landsat satellite imageries of three different time periods like Landsat TM of 1990, Landsat TM of 1999 and Landsat TM 2016 were used. Geographical distribution of snow cover reveals that in 1990 about 30.97% (678.87 km 2 ), in 1999 about 25.77% (564.92 km 2 ) area of the Gori Ganga watershed was under snow cover while in 2016 the snow cover was found only 15.08% (330.44 km 2 ). These data suggest that due to global warming about 348.43 km 2 snow cover of Gori Ganga watershed has been converted into non-snow cover area at an average rate 13.40 km 2 /year during the last 26 years.

Present research paper is an attempt to examine the dynamics of snow cover by using Normalized Difference Snow Index (NDSI) in Gori Ganga watershed, Kumaun Himalaya, Uttarakhand (India). For the study of snow cover of Landsat satellite imageries of three different time periods like Landsat TM of 1990, Landsat TM of 1999 and Landsat TM 2016 were used. Geographical distribution of snow cover reveals that in 1990 about 30.97% (678.87 km 2 ), in 1999 about 25.77% (564.92 km 2 ) area of the Gori Ganga watershed was under snow cover while in 2016 the snow cover was found only 15.08% (330.44 km 2 ). These data suggest that due to global warming about 348.43 km 2 snow cover of Gori Ganga watershed has been converted into non-snow cover area at an average rate 13.40 km 2 /year during the last 26 years.

…………………………………………………………………………………………………….... Introduction:-
Snow is a key component for energy balance of the earth's i.e. environment, climate and a major source of fresh water in many Himalayan regions. The Himalayan glaciers and region home of permanent snowfields, which from the largest bodies of ice outside of polar ice caps (Raina and Srivastava, 2008). As a important regional water resource, the Himalayan snow caps and winter seasonal snow melts make an major contribution to the drinking water, agriculture, and hydropower supply for a populated region in downstream river basins (Immerzeel et al. 2010). Satellite-based remote sensing is a convenient tool for the study of cryosphere that allows to carry out investigations over large inaccessible areas and analysis has been done using moderate-resolution imaging spectroradiometer (MODIS) satellite data for the past 11 years (2000-2010) with the temporal snow cover being derived using the Normalized Difference Snow Index (Joshi et al. 2015).
In the recent past, satellite-based remote sensing techniques have been widely used globally for investigating glacier fluctuations and snow cover changes (Kulkarni and Rathore, 2003). Due to accessibility and difficult terrain, only limited numbers of glaciers are monitored continuously in Indian Himalaya after 2000 to 2017. The Gangotri glacier, Dokriani glacier, Pindari glacier, Milam glacier, Chorbari glacier, Satopant glacier, Bhagirathi Kharak glacier, Parbati glacier, Samudra Tapu Glacier, Chhota Shigri glacier and Kolahoi Glacier are the most studied glaciers with field and satellite data (Naithani et Table 1 present a compilation of recession of some selected glaciers in the Indian Himalaya.
The fundamental objective of the present paper is to study the snow cover dynamic of a Higher Himalayan watershed viz. the Gori Ganga.

Study Area
The study area, viz., the Gori Ganga Watershed   Figure 2 is presenting a flowchart of the methodology adopted. Additionally, the reflectance of clouds remains high in the SWIR band, thus NDSI allows discriminating some clouds and snow. NDSI is useful for the identification of snow and ice and for discriminating 32 snow/ice from most cumulus clouds. This method is generally used for snow cover mapping using satellite data (Kulkarni et

Result And Discussion:-
The results obtained through the analysis of NDSI imagery are diagrammatically illustrated in Figure 3 and 4 and are registered in Table 2 and 3. Figure 3 depicts distribution of NDSI variation while Figure 4 depicts geographical distribution of snow cover area in 1990, 1999 and 2016 in the study area. Figure 5 depicts dynamics of snow cover area in 1990 to 2016. A brief account of these results it's discussed in the following paragraphs.

Normalized Difference Snow Index (NDSI)
NDSI is useful for the identification of snow and ice and for discriminating snow/ice from most cumulus clouds. This method is generally used for snow cover mapping using satellite data (Kulkarni et al. 2006;Gupta et al. 2005;Negi et al. 2008). NDSI uses the high and low reflection of snow in visible (green) and shortwave infrared (SWIR) regions respectively and it can also delineate and map the snow in mountain shadows. Additionally, the reflectance of clouds remains high in the SWIR band. Thus NDSI allows discriminating clouds and snow. NDSI is defined by the following relation and it ranges from -1 to +1 (Kulkarni et al., 2002).
Green -SWIR Green + SWIR In the methodology described for NDSI using Landsat data. Using ArcGIS software, three different years (1990,1999, and 2016) Landsat data were taken for calculating the NDSI index for Gori Ganga watershed. A threshold value for NDSI of 0.4 is defined for the imageries of different sensors (Xiao et al. 2001). To handle the mixed area, the threshold value of NDSI was lowered from 0.4 to 0.1 (Klein et al. 1998). Figure 3 is the normalized difference snow index which is based on Landsat-8 satellite imageries of three different time periods, i.e., Landsat TM of 1990, 1999 and 2016.

Status of Snow Cover
From the Figure 4 which is based on NDSI values of Figure 3, area under snow cover for different years was worked out which is presented in Table 2. Table 2

Shifting in Snow Cover
The data presented in Table 3 suggest that due to global warming the snow cover area in the Gori Ganga watershed has been shifted towards higher elevation and has been depleted considerably during the last two and half decades.
Result reveal that during 1990 to 2016, about 348.43 km 2 snow cover of the Gori Ganga watershed has been converted into non-snow cover area from the snow cover area at an average rate of 13.40 km 2 /year ( Table 3). The change from snow cover area to non-snow cover area during 1990 to 1999 was found about 104.64 km 2 at the rate of 11.63 km 2 /year and 1999 to 2016 was found about 243.79 km 2 at the rate of 14.34 km 2 /year, respectively (Table 3).

Conclusion:-
The present study was carried out in a Kumaun Himalayan watershed, viz., Gori Ganga watershed. The study reveals that from 1990 to 2016 about 348.43 km 2 area of the watershed has been converted into non-snow cover area from snow cover area. With the help of these data, it can be extrapolated that the snow cover area in Gori Ganga watershed is depleting at the average rate of 13.40 km 2 per year. It is evident from present study that the snow cover area is decline confirmed steadily in the Kumaun Himalaya due to global warming. If the trends of snow cover depletion, the water resource of the region will be in danger which may result in severe environmental degradation, social disruption and ecological damages in the Kumaun Himalayan region. This study has well demonstrated the applications of geospatial technology in studying snow dynemics.